How has the increase of Automated Trading Systems (ATS) influenced the futures market?
\[ \text{Performance} = \text{Skill} \times \sqrt{\text{Breadth}} \]
Two extremes
Single bet: \(0.51 \times 1000 + 0.49 \times (-1000) = 20\)
Multi bet: \(1000 \times [0.51 + 0.49 \times (-1)] = 20\)
The same expected return
Single bet: 49%
Multi bet: \(0.49 \times 0.49 \times \dots \times 0.49 = 0.49^{1000} \approx 0\)
\[ \text{risk} := \text{std}\left\{1,-1,-1,1, \dots, 1 \right\} = 1 \]
\[ \begin{align} \text{risk} &:= \text{std}\left\{1000,0,0,0, \dots, 0 \right\} = 31.62 \\ \text{risk} &:= \text{std}\left\{-1000,0,0,0, \dots, 0 \right\} = 31.62 \end{align} \]
Just like Sharpe Ratio
Single bet: \(\text{SR}_{\text{single}} = \frac{20}{31.62} =0.63\)
Multi bet: \(\text{SR}_{\text{multiple}} = \frac{20}{1} =20\)
\(20 = 0.63 \times \sqrt{1000}\)
\(\text{SR}_{\text{multiple}} = \text{SR}_{\text{single}} \times \sqrt{\text{Bets}}\)
\(\text{Performance} = \text{Skill} \times \sqrt{\text{Breadth}}\)
We use insights gained from years of fundamental trading to inspire bespoke quantitative strategies that are applied to a large collection of commodity markets
How to obtain a stationary time series
| Statistic | BB1 | BB2 |
|---|---|---|
| Annualized Return | 5.480 | 19.210 |
| Annualized Sharpe (Rf=0%) | 0.662 | 1.239 |
| Annualized Std Dev | 8.270 | 15.500 |
| Average Negative Month Return | -1.572 | -2.821 |
| Average Positive Month Return | 1.933 | 4.380 |
| Maximum Drawdown | 26.683 | 39.529 |
| Maximum Drawdown/Annualized Return | 4.869 | 2.058 |
| Number of Negative Months | 103.000 | 95.000 |
| Number of Positive Months | 147.000 | 154.000 |
Literature on extracting carry from futures:
Literature on applying machine learing techniques in algorithmic trading:
| Statistic | TR1 |
|---|---|
| Annualized Return | 18.930 |
| Annualized Std Dev | 17.800 |
| Annualized Sharpe (Rf=0%) | 1.064 |
| Maximum Drawdown | 26.683 |
| Maximum Drawdown/Annualized Return | 1.410 |
| Number of Positive Months | 142.000 |
| Number of Negative Months | 107.000 |
| Average Positive Month Return | 5.710 |
| Average Negative Month Return | -3.497 |
Strategy not yet live.
Strategy not yet live.
| Statistic | 1998- | 2008- | 2015- |
|---|---|---|---|
| Annualized Return | 19.930 | 18.960 | 5.900 |
| Annualized Sharpe (Rf=0%) | 1.701 | 1.644 | 0.634 |
| Annualized Std Dev | 11.720 | 11.530 | 9.310 |
| Average Negative Month Return | -2.107 | -2.258 | -2.304 |
| Average Positive Month Return | 4.055 | 4.156 | 2.835 |
| Maximum Drawdown | 18.722 | 13.709 | 12.802 |
| Maximum Drawdown/Annualized Return | 0.939 | 0.723 | 2.170 |
| Number of Negative Months | 100.000 | 54.000 | 23.000 |
| Number of Positive Months | 156.000 | 83.000 | 30.000 |
| Statistic | S&P500 | PSQCF | PSQCF and S&P500 |
|---|---|---|---|
| Annualized Return | 5.410 | 18.610 | 11.230 |
| Annualized Sharpe (Rf=0%) | 0.368 | 1.255 | 1.028 |
| Annualized Std Dev | 14.710 | 14.830 | 10.920 |
| Average Positive Month Return | 3.069 | 3.988 | 2.796 |
| Avereage Negative Month Return | -3.583 | -2.097 | -2.019 |
| Number of Negative Months | 95.000 | 101.000 | 96.000 |
| Number of Positive Months | 154.000 | 148.000 | 153.000 |
| Worst Drawdown | 52.556 | 19.692 | 32.728 |
| Statistic | S&P500 | PS Multi Strategy | PS Multi Strategy and S&P500 |
|---|---|---|---|
| Annualized Return | 12.890 | 15.410 | 14.470 |
| Annualized Sharpe (Rf=0%) | 1.139 | 1.524 | 1.931 |
| Annualized Std Dev | 11.320 | 10.110 | 7.490 |
| Average Positive Month Return | 2.794 | 2.860 | 2.151 |
| Avereage Negative Month Return | -2.385 | -1.705 | -1.389 |
| Number of Negative Months | 32.000 | 34.000 | 27.000 |
| Number of Positive Months | 64.000 | 62.000 | 69.000 |
| Worst Drawdown | 17.028 | 6.905 | 7.038 |
Combining a
to investing in commodities we create a product with
that gives superior risk adjusted returns.